Predictors of Hospitalization of Individuals With First-Episode Psychosis: Data From a 2-Year Follow-Up of the RAISE-ETP

Inpatient hospitalization can be very disruptive to the goals (e.g., schooling) of young people with first-episode psychosis (FEP), and it is often experienced as traumatic (1, 2). Caregivers frequently experience distress and negative outcomes (e.g., stigma, changes in relationships) associated with their family member’s hospitalization (3, 4), in addition to the positive changes that hospitalization can foster (4). From a services perspective, hospitalization costs are a major component of the cost of first-episode care in early intervention services (EIS). The mean cumulative cost for psychiatric inpatient treatment over five years for the OPUS intervention in Denmark was €58,502 of the total treatment cost of €123,683 (5) and, over 18 months in the British Lambeth Early Onset trial, £6,103 of the total cost of £11,685 (6). In the United States, the average cost of EIS NAVIGATE treatment every six months in the Recovery After an Initial Schizophrenia Episode–Early Treatment Program (RAISE-ETP) study was $9,018, of which $4,709 was hospitalization costs (7). Further decreasing hospitalization costs could bolster the cost-effectiveness and thus the sustainability of EIS.

A meta-analysis (8) of EIS trials (9–17) found that compared with usual care, EIS were associated with a reduced risk of hospitalization (rates presented in Table 1). Hospitalization utilization varied by follow-up duration and health system. Over a two-year period, the lowest percentage of participants hospitalized even with EIS is 33%. Hospitalization rates with EIS treatment provided outside of a randomized treatment trial context (18, 19) have also been reported, and these are similar to the rates found in the randomized trials.

To identify targets for the development of interventions to decrease the risk of hospitalization for individuals with FEP, we examined data from the RAISE-ETP study (ClinicalTrials.gov registration NCT01321177). RAISE-ETP compared a multielement treatment model (20) for FEP with usual care. RAISE-ETP’s background, rationale, and design have been published (21), as have its CONSORT flow diagram, participant characteristics, and two-year outcomes (16). The advantage of using RAISE-ETP data to find hospitalization predictors for a population with an already relatively low hospitalization rate is that the data cover a 2-year follow-up of a population with a low hospitalization rate compared with that observed in other studies of similar duration.

Methods

Participants

RAISE-ETP enrolled English-speaking individuals between ages 15 and 40 years with a DSM-IV diagnosis of schizophrenia, schizoaffective disorder, schizophreniform disorder, brief psychotic disorder, or psychotic disorder not otherwise specified. Individuals were excluded if they had affective psychosis, substance-induced psychotic disorder, psychosis resulting from a general medical condition, clinically significant head trauma, or a serious general medical condition. All participants had experienced only one episode of psychosis (although this episode might have resulted in multiple hospitalizations) and had taken six or fewer months of lifetime antipsychotics.

Written informed consent was obtained from adult participants and from legal guardians of participants younger than age 18, who provided written assent. The institutional review boards of the coordinating center and the participating sites approved the study. The National Institute of Mental Health Data and Safety Monitoring Board provided study oversight.

Clinical Sites

Thirty-four outpatient community mental health centers in 21 states were selected via a national search. Site eligibility criteria included experience treating individuals with schizophrenia; interest in offering EIS for FEP; sufficient staff to implement the experimental intervention; ability to recruit an adequate number of participants; and institutional assurance that research assessments would be completed. Academic centers or sites with existing first-episode programs were excluded. All participants were outpatients at the time of their baseline assessment.

RAISE-ETP used cluster randomization (i.e., randomization by clinic rather than individual participant; 22). The study statisticians randomly assigned 17 of the clinics to the experimental intervention and 17 to standard care.

Interventions

NAVIGATE (20), the experimental EIS, is team based and includes four interventions: personalized medication management, family psychoeducation, resilience-focused individual therapy, and supported education and employment (manuals available at www.raiseetp.org). The primary outcome measure and therefore the goal of RAISE-ETP was improved quality of life, not the prevention of hospitalization per se. These goals are not mutually exclusive in that hospitalization impedes progress toward improving quality of life. With respect to factors that might influence hospitalization risk, personalized medication management included assessment of symptoms, side effects, adherence, and substance use at each visit. The psychosocial interventions included illness management strategies and modules on adherence and making decisions about substance use. The control condition, community care, was psychosis treatment determined by individual and clinician choice and service availability.

Trial Duration

Enrollment occurred between July 2010 and July 2012. The minimum potential trial duration for each participant was two years (longer for early enrollees); these two years are the focus of this report. Study assessments were suspended during periods of incarceration or hospitalization but resumed after release or discharge. Research assessments continued even if participants discontinued NAVIGATE or community care treatment.

Assessment Strategy and Measures

Centralized assessors, masked both to individual treatment assignments and to the overall study design, administered the following measures via live, two-way video conferencing: the Structured Clinical Interview for DSM-IV (SCID-IV; 23) for diagnosis and to obtain the information required to determine the duration of untreated psychosis (DUP), the Positive and Negative Syndrome Scale (PANSS; 24), the Clinical Global Impressions Severity Scale (CGI-severity; 25), the Calgary Depression Scale for Schizophrenia (CDSS; 26), and the Heinrichs-Carpenter Quality of Life Scale (QLS; 27), which was the primary outcome measure. Remote assessment via two-way video conferencing is comparable to face-to-face assessment in patient acceptability and reliability (28). The SCID-IV was completed at baseline; the other measures were completed every six months.

Site research assistants interviewed participants monthly with the Service Use and Resource Form (SURF; 29, 30) to capture psychiatric inpatient and emergency services use and self-reported days of alcohol or drug use. Emergency department visits that lasted more than 24 hours were considered hospitalizations. Participant-reported assessments allowed us to obtain information not only about treatment that participants received at their study site but also about treatment they received outside the site (e.g., inpatient admission at another agency). Participant self-report has proven to be generally accurate (29). The outcome of interest for this article was mental health hospitalization occurring after study entry (all participants were outpatients at this time point). We obtained data on hospitalizations before study entry through individual and family interviews and medical record search; these data were examined as predictors of hospitalization during the study.

At baseline, 3-month follow-up, 6-month follow-up, and every 6 months thereafter, participants completed the Intent to Attend measure (31), the Adherence Estimator (32), Brief Evaluation of Medication Influences and Beliefs (BEMIB) scale (33), seven items from the Stigma Scale (34), a subset of the Perceived Well-Being Scale (35), the six-item Autonomy Support Scale short-form version of the Health Care Climate Questionnaire (36), and an abbreviated version of the Mental Health Recovery Measure (37). They also rated their current state of mental and emotional health on a scale ranging from 1, worst possible, to 100, perfect health, and how they felt about their life as a whole on a scale ranging from 1, terrible, to 7, delighted.

Statistical Analysis

We used time-to-event analysis for hospitalization. The Cox proportional hazard model was used with site included as a frailty term to account for clustering of individuals within site. Clustered randomized trials often have a limited number of clusters, and this can result in an imbalance on baseline measures between the randomized treatment conditions. Such imbalances may confound the relationship between treatment and individual-level outcomes. Therefore, significant baseline differences between the treatment conditions were included as adjustment variables. We assessed whether the two treatment conditions differed in hospitalization and adjusted for the baseline covariates of gender, student status at entry, and total PANSS score, which were found to be significantly associated with treatment condition in previously reported analyses (16).

For analysis of the longitudinal assessments, we constructed time-varying predictor variables that consisted of the results of the assessment concurrent with or, if not concurrent with, the assessment closest in time to an individual’s first hospitalization. The severity and intensity of a factor often change over time. The use of time-varying predictor variables allowed us to examine the effects of a variable of interest assessed at the time closest to a hospitalization, when it might have had the greatest impact on hospitalization. For example, if an individual had a hospitalization at month 18, we used the month 18 assessment; if those results were not available, we used the closest preceding assessment to month 18. For individuals with no hospitalizations, we used the results from the last assessment.

To determine hospitalization predictors, we first performed univariate analyses using a Cox proportional model with frailty of site for each candidate of the baseline and time-varying covariates. In developing the multivariate model for baseline predictors, the variables we screened for entry into the analysis were those with significant or trend-level associations in the univariate analyses of baseline predictors. Because inclusion of correlated variables can result in unstable multivariate correlations, we used several criteria to determine which correlated variables to enter. We gave preference to variables that would provide more clinically meaningful information if associations with hospitalization were found (e.g., we preferred factor scores over total scores because the former describe more circumscribed symptoms than the latter and could provide more precise targets for intervention development). The final set of baseline variables for model entry included DUP longer than 74 weeks, number of prior hospitalizations, PANSS positive and excited factors, CDSS, and the Perceived Well-Being Scale.

The strategy for developing the final multivariate models that integrate both baseline and time-varying variables was to consider for entry first, the baseline variables with significant associations with hospitalization in the multivariate baseline analysis and second, the time-varying variables with significant associations with hospitalization from the univariate analyses of time-varying variables and hospitalization. By examining the correlations among variables and using our strategy of considering clinical meaningfulness in variable selection, we developed two groups of variables for entry into the analyses. Both groups included the baseline predictors DUP and number of prior hospitalizations and the time-varying predictors of days of illegal drug use and being a student or working. In addition, in analysis 1 we added time-varying variables rated by the central assessors—the PANSS positive and excited factors and the CDSS—plus the self-rated Adherence Estimator. In analysis 2, we added time-varying variables rated by the participant, the BEMIB, and the Perceived Well-Being Scale total score. The participant-rated Adherence Estimator and BEMIB measures were highly correlated (r=−.43). Thus, we could include only one of them in analysis 2, which focused on participant-rated assessment. We chose to include the BEMIB in analysis 2 because it taps participants’ beliefs about the value of medication for themselves. The Adherence Estimator taps general attitudes toward medication and was not significantly correlated with any of the central assessor-rated variables; it was included in analysis 1.

For each analysis, we checked proportional hazard assumptions by dividing time into 6-month intervals and assessing whether the coefficients were statistically different across time intervals.

Results

Participants

Characteristics of the full RAISE-ETP sample of 404 individuals have been published (16). Some participants did not have any postbaseline assessments. Supplemental Table 1, which presents the characteristics of the 382 participants who had at least one postbaseline assessment and thus have postbaseline hospitalization data, is available as an online supplement to this article. Overall, the 382 participants were young (mean±SD age =23.2±5.1 years), mostly male (73%, N=279), and of diverse racial background. Outpatient community center sites typically receive most of their FEP referrals from inpatient units. Consistent with this pattern, only 84 participants had never had an inpatient psychiatric hospitalization.

Psychiatric Hospitalization

Of the participants, 112 had at least one psychiatric hospitalization during the two-year observation period. On the basis of a survival analysis, 34% of NAVIGATE and 37% of community care participants had a hospitalization (this estimate is the same as that previously reported [16] for the sample of 404 individuals as a result of censoring effects with survival analysis of individuals who did not have postbaseline assessments). Hospitalization rates did not differ between participants receiving NAVIGATE and those receiving community care treatment (hazard ratio=0.892, χ²=0.35, df=1, p=.557).

Factors Associated With Hospitalization

Baseline variables.

Table 2 presents the associations between baseline characteristics and psychiatric hospitalization during the follow-up, based on univariate analyses. We found significant associations for having had a hospitalization before study entry; scores on the Wallwork (38); positive, excited, and depressed factors of the PANSS; CDSS total score; CGI-severity; and participants’ ratings of the Perceived Well-Being Scale and their current state of mental health. Other variables with trend-level associations (p<.1) were DUP (dichotomized at the median value of 74 weeks [16, 39]), working at the time of study entry, Heinrichs-Carpenter QLS Instrumental Role, PANSS total score, and the Mental Health Recovery Measure.

Table 3 presents the results of multivariate analyses of the association between baseline variables and subsequent hospitalization. DUP, prior hospitalizations, the PANSS excited factor, and Perceived Well-Being Scale mean score were all significant predictors of subsequent hospitalization.

Time-varying variables.

Table 4 presents the univariate analyses of the associations between the time-varying variables and hospitalization. We found significant associations among currently working; being a student or worker; QLS total and Instrumental Role scores; PANSS total and positive, excited, and depressed factor scores; CDSS; CGI-severity; BEMIB; Mental Health Recovery Measure scores; Perceived Well-Being Scale scores; current state of mental health; life as a whole; number of days of illegal drugs; and Adherence Estimator risk scores and subsequent hospitalization.

Multivariate models integrating baseline and time-varying variables.

As described in the Statistical Analysis section, we tested two analysis models. As presented in Table 5, both analyses found significant associations between hospitalization during the study and having had multiple hospitalizations before study entry and time-varying days of illegal drug use. We found additional significant associations with PANSS positive symptoms in analysis 1 and with DUP of more than 74 weeks and BEMIB scores in analysis 2.

Discussion

Even though RAISE-ETP participants experienced a relatively low hospitalization rate, we were able to identify predictors of hospitalization. At study baseline, those with longer DUP, more hospitalizations before study entry, symptoms of excitement, and lower reported well-being were more likely to be hospitalized during the two-year treatment period. When we added information gathered across the trial to our multivariate analyses, longer DUP and history of hospitalization before study entry continued to influence risk of hospitalization, but positive psychosis symptoms closer to the time of hospitalization, use of illegal drugs, and beliefs about medication were now predictive.

Our results are generally consistent with the predictors of hospitalization found in other first-episode trials or longitudinal follow-up studies with the exception of the findings regarding DUP. Our finding that individuals with prior hospitalizations were at increased risk for hospitalization during the trial is consistent with the results of other studies of first-episode populations over the first years of treatment (18, 40). This vulnerability may persist for longer periods; Mortensen and Eaton (41) found that, over the first 10 years after a first admission for schizophrenia, time to readmission became shorter as the number of admissions increased. As with our study, other first-episode studies have identified psychosis (18, 42–44), excitement symptoms (45), use of illegal drugs (11, 46–50), and poor medication adherence (48, 51–54) as hospitalization risk factors. In our study, individual self-report of adherence over time predicted hospitalization at a trend level in univariate analyses, and its association with beliefs about medication was significant in the multivariate analyses.

DUP is a predictor of several outcome domains of FEP (55, 56). In contrast, no association between DUP and hospitalization risk has been found among several first-episode populations (18, 43, 44, 57), although we and Sipos and colleagues (45) have found an association. These studies come from a variety of countries with different health systems and pathways to care that may have contributed to the variability of results. Moreover, comparison across studies is complicated by the often skewed distribution of DUP. For example, although the median duration in RAISE-ETP was 74 weeks, 23.8% of participants had a DUP duration of 3 months or less, the target DUP in the consensus statement (58) of the World Health Organization and the International Early Psychosis Association. Nevertheless, the DUP in all of the studies that did not find an association with hospitalization risk was shorter than the median 74 weeks in RAISE-ETP. It is possible that once DUP is shortened to a particular degree, further DUP shortening does not decrease hospitalization risk. Research is needed to clarify the effect of DUP on first-episode hospitalization risk and determine what, if any, is the minimum DUP associated with increased hospitalization risk.

Our findings have implications for future efforts to enhance EIS. Individuals enter outpatient treatment with an already fixed number of prior hospitalizations and DUP. Changing these factors will require public health initiatives and innovative outreach strategies (59) to facilitate earlier entry into treatment. These baseline characteristics can also be used to identify individuals at increased hospitalization risk who might be candidates for interventions specifically targeted to decrease that risk, such as individualized relapse prevention plans. Current EIS models include interventions to help individuals decrease substance misuse, achieve symptom reduction, and understand medications and adherence. Some of these interventions have low participation by individuals who would benefit from them (e.g., substance misuse interventions [60]), suggesting that more effort may be needed to motivate individuals to use available services. Further direct development or refinement of the interventions, such as innovative strategies to support medication adherence (61–63), also should be considered.

To be a RAISE-ETP site, facilities had to have an interest in participating in such a study and the clinical and administrative infrastructure to provide NAVIGATE treatment if the site was randomly assigned to provide it. A limitation to generalization of our finding to the entire range of community clinics is that the site inclusion criteria may have resulted in the selection of clinics with above-average motivation and resources to serve individuals with FEP. Our study sites were outpatient facilities. Our data do not address predictors of hospitalization for individuals experiencing FEP who never receive outpatient treatment (e.g., those whose treatment occurs only on inpatient units).

Conclusions

Current treatment practices can reduce the risk of hospitalization of individuals with FEP, but further efforts at reducing hospitalization risk are needed. Potential targets for further intervention development include reducing the length of DUP and the number of hospitalizations before EIS care commences, decreasing substance misuse and symptoms, and enhancing adherence. Better intervention could enhance the impact of first-episode EIS treatment models and enhance outcomes for people with FEP treated through the use of other models.